Ensembling-mRBF-LSTM Framework for Prediction of Abnormal Traffic Flows

Traffic flow forecasting is one of the key issues in smart traffic systems. The process of traffic flow changing involves a high degree of nonlinearity and randomness, environmental interference and measurement noise, which brings difficulties to accurate traffic flow prediction. Aiming at improving the accuracy of short-term traffic flow prediction, this paper proposes a method called Basis Prediction method. A raw traffic flow series can be deemed as a summation of a basis series that indicates the changeable trend of the traffic flow and a deviation series that represents the random interference information involved in the flow. The basis series comprises mainly low-frequency signals and the deviation series is composed of some high-frequency signals. Based on an appropriate extraction of the basis series, prediction merely of the basis series brings more precise prediction of the raw traffic flow. This paper suggests adopting wavelet decomposition to obtain the basis series and the deviation series from the raw traffic flow. To predict the basis series effectively, two algorithms, local weighted partial least squares (LW-PLS) and Kalman filtering, are adopted separately and a comparison between them is also provided. In this paper, real data of traffic flow of Xinbei city in Taiwan was collected and used for validation of the proposed basis prediction method. The results show that the use of that proposed method improves the accuracy of short-term traffic flow prediction by about 2% on average.

Geographic division directly affects regional traffic flow prediction result. Classical methods like latitude-longitude meshing and POIs (point of interest) clustering can’t effectively describe traffic transfer between regions. In this study, a topology map is generated by road segment clustering. Several road segments in a network are the basic clustering units. The adjacency, connectivity, and congestion between road segments and the congestion degree of road segments are made as attributes of the cluster similarity measurement. Road segments within urban areas are clustered by spectral clustering algorithm. Topological map composes converged clustering centers and edges made of connection ports among the roads of each node. The experiment data consists of 120,000 road segments and vehicles’ trajectory dataset in Beijing. Topological maps are generated at different granularities. The results show that the topological map has a better visualization and the amount of traffic flow information is superior to POIs clustering and meshing.

Traffic flow has the characteristics of complex spatial dependence and temporal dependence. Deep learning as a traffic flow prediction method can make full use of the temporal and spatial characteristics of traffic flow. In this paper, the road network is abstracted into a graph structure, the size of the graph structure is dynamically changed, and the graph convolutional neural network (GCN) and the long short term memory network (LSTM) are used to capture the temporal and spatial characteristics of traffic flow to solve the traffic flow prediction problem. Based on the data of vehicle speed in California bay area, the experiment is divided into three prediction scales. The effectiveness of the traffic flow prediction model is verified by experimental comparison.

A Single traffic flow prediction method has weak applicability for short-time traffic flow prediction. In order to adapt to the needs for traffic guidance and signal control, a short-time traffic flow RBF neural network model combination prediction method based on wavelet de-nosing processing is put forward. First, the traffic flow data are decomposed and reconstructed by using the wavelet transform technique. Then Under the intensive analysis the characteristics of short-time traffic flow, the low frequency outline signal and high frequency detail signal are fitted respectively by using two different RBF neural network models, and particle swarm optimization (PSO) algorithm is proposed to train RBF neural network. The confirmation analysis is carried on with traffic flow data from typical roads in some city urban districts. The results show that the precision of combination prediction method is significantly improved.

To more effectively capture the periodic and dynamic changes in urban traffic flow and the spatiotemporal correlation of complex road networks, a new traffic flow prediction method, the Enhanced Spatiotemporal Graph Convolutional Network Encoder-Decoder Model (ESGCN-EDM), is proposed. The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. The aPFGCN module effectively reduces the dimensionality of features and decreases model complexity to obtain the final node feature representation by personalizing the adjustment of node influence coefficients and applying Fourier transform and inverse transform techniques. Additionally, by incorporating an attention mechanism, it enhances the model’s ability to focus on important information and effectively captures the spatial topological relationships within the traffic network. The TCBiL module integrates 1D convolution with BiLSTM to form a unified temporal feature extraction module. The 1D convolution is utilized to extract local features from the time series, while the BiLSTM captures long-term dependencies within the time series. This allows for simultaneous feature extraction and temporal modeling, thereby enhancing the model’s efficiency and performance, and strengthening its ability to model time series. In the encoder part of the ESGCN-EDM, the aPFGCN is combined with the TCBiL to handle the spatiotemporal coupling interactions of the road network. The decoder part then performs multi-step predictions based on spatiotemporal sequences using the TCBiL and CNN, generating high-dimensional representations. Extensive experiments conducted on two real-world road traffic datasets demonstrate that the ESGCN-EDM model consistently outperforms other benchmark models in 1-hour, 30-minute, and 15-minute traffic flow predictions. Specifically, on the PeMSD8 dataset, the model achieves reductions in MAE, RMSE, and SMAPE by 7.9%, 2.1%, and 16.9%, respectively, compared to the AMRGCN model for 1-hour predictions. Similarly, on the PeMSD4 dataset, the model reduces MAE, RMSE, and MAPE by 1.8%, 1.1%, and 3.0%, respectively. These results validate the efficacy of the proposed model and its ability to significantly enhance the accuracy of traffic flow forecasting.

Traffic flow has the characteristics of randomness, nonlinearity and periodicity. Using deep learning as the method of traffic flow prediction can make full use of traffic flow characteristics for prediction. In this paper, a recurrent neural network-LSTM (Long Short-Term Memory) model and its variant GRU (Gated Recurrent Units) are used to solve the traffic flow prediction problem. According to the road traffic information collected by the automatic measurement station (LAM) of the Dutch traffic management department, the vehicle traffic flow data has been predicted every five minutes. It’s verified that the GRU traffic flow prediction model performs well by comparing the prediction accuracy of LSTM and GRU.

Accurate short-term traffic flow prediction is the foundation of the efficient and proactive management of freeway networks, especially on the abnormal traffic states. The relationship between traffic flow on the current section and the upstream stations can be used for predicting short-term traffic flow. In this paper, we reveal this relationship by the traffic flow structure pattern. The structure pattern can be drawn from real freeway toll data and a few video detective cameras on the freeway segments. Based on the stability pattern, a new traffic flow prediction algorithm has been proposed. Experimental based on real data showed that the prediction method based on structure pattern is an effective approach for traffic flow prediction, especially on the abnormal traffic state.

Air traffic is increasing worldwide at a steady annual rate, and airport congestion is already a major issue for air traffic controllers. The traditional method of traffic flow prediction is difficult to adapt to complex air traffic conditions. Due to its self-learning, self-organizing, self-adaptive and anti-jamming capability, the hybrid fuzzy neural network can predict more effectively the air traffic flow than the traditional methods can. A good method for training is an important problem in the prediction of air traffic flow with neural network. This paper will try to find a new model to solve the traffic flow prediction problem by hybrid fuzzy neural network.

Traffic flow forecasting is crucial for improving urban traffic management and reducing resource consumption. Accurate traffic conditions prediction requires capturing the complex spatial-temporal dependencies inherent in traffic data. Traditional spatial-temporal graph modeling methods often rely on fixed road network structures, failing to account for the dynamic spatial correlations that vary over time. To address this, we propose a Transformer-Enhanced Adaptive Graph Convolutional Network (TEA-GCN) that alternately learns temporal and spatial correlations in traffic data layer-by-layer. Specifically, we design an adaptive graph convolutional module to dynamically capture implicit road dependencies at different time levels and a local-global temporal attention module to simultaneously capture long-term and short-term temporal dependencies. Experimental results on two public traffic datasets demonstrate the effectiveness of the proposed model compared to other state-of-the-art traffic flow prediction methods.

Short-term traffic flow prediction plays an important role in intelligent transportation system. Numerous researchers have paid much attention to it in the past decades. However, the performance of traditional traffic flow prediction methods is not satisfactory, for those methods cannot describe the complicated nonlinearity and uncertainty of the traffic flow precisely. Neural networks were used to deal with the issues, but most of them failed to capture the deep features of traffic flow and be sensitive enough to the time-aware traffic flow data. In this paper, we propose a deep bi-directional long short-term memory (DBL) model by introducing long short-term memory (LSTM) recurrent neural network, residual connections, deeply hierarchical networks and bi-directional traffic flow. The proposed model is able to capture the deep features of traffic flow and take full advantage of time-aware traffic flow data. Additionally, we introduce the DBL model, regression layer and dropout training method into a traffic flow prediction architecture. We evaluate the prediction architecture on the dataset from Caltrans Performance Measurement System (PeMS). The experiment results demonstrate that the proposed model for short-term traffic flow prediction obtains high accuracy and generalizes well compared with other models.

Intelligent traffic signal control is one of the important means to ensure traffic safety. Effective signal control can make traffic flow fast and smooth, which first needs current and future traffic information. As the control of one intersection may affect adjacent intersections, this paper proposes to predict future traffic flow with consideration of multi-intersections. It can dynamically improve traffic conditions and reduce traffic congestion. Based on various nonlinear spatial relationships at correlated multi-intersections and the potential time-dependent relationship in traffic volume, a traffic flow prediction method named CNNformer which combines transformer with CNN, is proposed. The convolution neural network (CNN) and transformer are used to extract the spatial and temporal features of correlated multiple intersections. The learnable time code is embedded into transformer’s location code, and the location information and time information are injected into the model to help it learn the time characteristics of traffic volume. This study also considers the impact of cyclical traffic flow pattern and proposes CNNformer+. In the method, all of the data from the previous time window, as well as the data from the prior week and month, are correspondingly entered into the network. Finally, the output is generated through average pooling, realizing the learning of cyclical traffic flow characteristics. In the experiment, CNNformer+ and the state-of-the-art traffic flow prediction methods are compared using simulated data. The results show that the proposed model outperforms the existing models in prediction accuracy and efficiency.

With the acceleration of urbanization and the continuous growth of traffic flow, accurate prediction of highway traffic flow has become a key demand for optimizing traffic management and alleviating congestion. Traditional prediction methods are often inefficient in dealing with complex traffic patterns, while deep learning technology brings new solutions for traffic flow prediction with its powerful feature learning and pattern recognition capabilities. This paper reviews deep learning applications' current state and future trends in highway traffic flow prediction. It introduces deep learning technology and its advantages in traffic flow prediction. The paper then analyzes the specific applications of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and their variants in data processing, feature extraction, and prediction performance. The challenges currently faced, such as data preprocessing and the capture of long-term dependencies, are also discussed, along with proposed solutions. Finally, the paper looks forward to the future development of deep learning in the field of traffic flow prediction, highlighting its importance in improving traffic management efficiency.

Traffic flow prediction can guide the rational layout of land use. Accurate traffic flow prediction can provide an important basis for urban expansion planning. This paper introduces a personalized lightweight federated learning framework (PLFL) for traffic flow prediction. This framework has been improved and enhanced to better accommodate traffic flow data. It is capable of collaboratively training a unified global traffic flow prediction model without compromising the privacy of individual datasets. Specifically, a spatiotemporal fusion graph convolutional network (MGTGCN) is established as the initial model for federated learning. Subsequently, a shared parameter mechanism of federated learning is employed for model training. Customized weights are allocated to each client model based on their data features to enhance personalization during this process. In order to improve the communication efficiency of federated learning, dynamic model pruning (DMP) is introduced on the client side to reduce the number of parameters that need to be communicated. Finally, the PLFL framework proposed in this paper is experimentally validated using LPR data from Changsha city. The results demonstrate that the framework can still achieve favorable prediction outcomes even when certain clients lack data. Moreover, the communication efficiency of federated learning under this framework has been enhanced while preserving the distinct characteristics of each client, without significant interference from other clients.

Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting

Accurate traffic flow prediction is crucial for improving transportation efficiency. To improve the accuracy of traffic flow prediction, we developed a traffic flow prediction framework—namely, traffic flow multicomponent network—that appropriately processes the noise, volatility, and nonlinearity in traffic flow data. This framework comprises three components: a factor selection component, traffic flow decomposition component, and traffic flow prediction component. The factor selection component considers the dynamic effects of weather‐related, environmental, and spatiotemporal factors on traffic flow; it then extracts and analyzes factors exhibiting strong correlations with traffic flow. The traffic flow decomposition component optimizes the parameters of variational mode decomposition on the basis of the envelope entropy by using the sparrow search algorithm; it then transforms traffic flow into multiple intrinsic mode functions to enable accurate traffic flow prediction. Finally, the traffic flow prediction component constructs dynamic feature matrices by using a bidirectional gated recurrent unit model to identify relationships within the data. Moreover, it uses an attention mechanism to assign different weights to different features on the basis of the importance of these features to traffic flow prediction, thereby enabling the efficient processing of a large volume of data. The performance of the proposed framework was examined in experiments conducted on large volumes of traffic flow data with different time granularities. The results indicated that the proposed framework achieved high prediction accuracy and stability for various time granularities, data samples, dataset sizes, and noise conditions. Moreover, it generally outperformed existing traffic flow prediction models under all experimental conditions.